Single Cell Profiling of Signaling Pathways and Phenotypic

Marker Expression in Philadelphia-like B-cell Acute

Lymphoblastic Leukemia

by

Alicia Kilfoy

A thesis submitted in conformity with the requirements

for the degree of Master of Science

Graduate Department of Immunology,

Faculty of Medicine, University of Toronto

© Copyright by Alicia Kilfoy 2019

ii

Single Cell Profiling of Signaling Pathways and Phenotypic

Marker Expression in Philadelphia-like B-cell Acute

Lymphoblastic Leukemia

Alicia Kilfoy

Master of Science

Department of Immunology,

University of Toronto

2019

Abstract

B-cell Acute Lymphoblastic Leukemia (B-ALL) is the most common childhood cancer,

but also occurs in adults. It is caused by a broad spectrum of genetic alterations which are used for

risk stratification. Across all ages, the Philadelphia-like (Ph-like) subtype is associated with lower

survival rates. Kinase activating mutations that converge on either ABL or JAK2 signaling are

found in >80% of Ph-like cases. Although clinically approved targeted therapies are available for

both subgroups, there is currently no test to rapidly identify them. Therefore, I built a mass

cytometry panel to profile signaling and phenotypic markers in 7 BCR-ABL and 4 Ph-like samples.

I identified blasts with activating ABL or JAK2 mutations based on expression of pSTAT5 and its

reduction by targeted inhibitors. Furthermore, I distinguished Ph-like and BCR-ABL specific

clusters that differed in CD14 and CD127 expression. My thesis demonstrates how mass cytometry

can be used to identify Ph-like samples.

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Acknowledgments I entered this graduate program as a nervous undergrad student lacking confidence. Three

years later I leave with a new sense of confidence and resilience and for that I have so many people

to thank. First and fore mostly, I’d like to thank my supervisor Dr. Cynthia Guidos. I have learned

so much and grown immensely, both scientifically and personally, under your supervision. From

your attention to detail to valuable life lessons such as the importance of preparation, I am so

grateful for your continued support and mentorship. This project has been the most phenomenal to

be a part of. Although, I do have to say, I will be very happy to never titrate another antibody

again. To my committee members, Drs. Jayne Danska, Naoto Hirano and Michael Ratcliffe thank

you for your insight and knowledge. Your thoughts and opinions on not only my project but the

scientific method have helped propel my work forward. Particularly, I’d like to thank Dr. Danska

for her guidance and support. You and Cindy are true examples of strong women I admire and

aspire to be like. Most importantly, I will so miss cheering on the Raptors together. Go Raps Go!

To my most valued and beloved support network: my parents, boyfriend and younger

brother, words cannot describe how thankful I am for you. Thank you for always answering the

phone no matter the time of night, eagerly listening to me practice my presentations, and most

importantly never failing to make me laugh despite my mood.

To the Guidos/Danska lab members both present and past I have had the pleasure of

working with, I will miss you all dearly. I leave the lab not only with an MSc but with wonderful

friendships. To Dina Levi, I would not have completed this program without you. Thank you for

being the absolute best mother duck and taking me under your wing. To Mark Gower, you were

fabulous to work alongside and will be an excellent PI one day. To my other closest “labbies” Miki

Gams, Michelle Zuo, Frances Simbulan, Kirsty Bannerman, Jerry Shen, Tina Chen, and Greg

Chang thank you for always listening to my rants and importantly laughing at my jokes.

I am so sad to end this chapter of my life, but am entirely thankful for the memories and

lessons that came with it. To future students reading this I advise following the mantra of Dory,

one of the wisest fishes in the sea, and “Just Keep Swimming!” Alicia out!

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Data Acknowledgments I have a few people I’d like to thank and acknowledge for their contributions to this thesis.

First, Mark Gower who performed the hierarchical clustering in R and made the Dendrogram in

Figure 3.7. Second, I’d like to thank Anthony Zhao who created the platform I used to run

Phenograph analysis. Last, I’d like to thank Greg Chang who conjugated the antibodies and the

various members of the CyTOF team who collected the samples.

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Table of Contents

Abstract..........................................................................................................................................ii Acknowledgments.........................................................................................................................iii Table of Contents...........................................................................................................................v List of Figures and Tables...........................................................................................................vii List of Abbreviations..................................................................................................................viii Chapter 1: Introduction 1.1 Overview...................................................................................................................................2

1.2 B-cell Acute Lymphoblastic Leukemia…………....................................................................3

1.3 BCR-ABL B-ALL......................................................................................................................4 1.4 Philadelphia-like B-ALL...........................................................................................................5 1.5 High Dimensional Immune Phenotyping of Leukemia Using Mass Cytometry........................................................................................................................................7

1.6 Project Rationale and Goals.......................................................................................................9 Chapter 2: Materials and Methods 2.1 Cell Culture .............................................................................................................................12 2.2 Peripheral Blood Mononuclear Cells (PBMC) from Non-Leukemic Donors............................................................................................................................................12 2.3 B-ALL Patient Samples ..........................................................................................................12 2.4 Antibodies................................................................................................................................13 2.5 Cell Staining for Mass Cytometry...........................................................................................13 2.6 De-barcoding and Clustering Analysis....................................................................................15 2.7 Statistical Analysis...................................................................................................................16

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Chapter 3: Results 3.1 Optimization and Validation of CyTOF Antibodies...............................................................21 3.2 Validation Pilot on BCR-ABL Samples...................................................................................25 3.3 CyTOF Profiling of Signaling Activity in Ph-like vs BCR-ABL Subgroups...........................31 3.4 Unsupervised Clustering Algorithm Reveals Differential Expression of Phenotypic Markers in Ph-like and BCR-ABL Samples .................................................................................................35 3.5 Conclusions and Summary......................................................................................................46

Chapter 4 Discussion

4.1 Overview..................................................................................................................................48 4.2 BCR-ABL Phenotypic and Signaling Heterogeneity................................................................49 4.3 RUX Sensitive pSTAT5 as a Ph-like CRLF2/JAK2 B-ALL Marker......................................50 4.4 BCR-ABL Specific Cluster Expressed Significantly Higher CD14, CD34 and CD19............51 4.5 Ph-like Specific Cluster Expressed Significantly Higher CD127 and HLA-DR....................52 4.6 Class Defining Clusters Could be Subclones Present at Diagnosis.........................................52 4.7 Conclusions and Future Directions .........................................................................................53 References.....................................................................................................................................56

vii

List of Tables and Figures

Chapter 2: Materials and Methods Table 2.1 Immunophenotyping Antibodies...................................................................................17 Table 2.2 Antibodies Specific for Signaling and Apoptosis Markers...........................................18 Table 2.3 Signaling Agonists and Antagonists..............................................................................19

Chapter 3: Results Figure 3.1: Development and optimization of a 36-Marker CyTOF panel...................................23 Figure 3.2: Pilot CyTOF analysis of BCR-ABL samples...............................................................27 . Figure 3.3: Highly heterogeneous marker expression in BCR-ABL samples................................28 Figure 3.4: Signaling pathway activation in BCR-ABL samples...................................................29 Figure 3.5: Patient cohort for Ph-like vs BCR-ABL experiments..................................................32 Fig 3.6: DAS and RUX decrease pSTAT5 more robustly in BCR-ABL and Ph-like CRLF2/JAK2 samples respectively................................................................................................33 Figure 3.7: Hierarchical clustering of phenotypic markers by sample group..................................38 Figure 3.8: Unbiased identification of non-leukemic clusters.......................................................39 Figure 3:9: PG tSNE visualization of PG clusters in non-leukemic vs leukemic PBMC samples ..........................................................................................................................................40 Figure 3.10: PG tSNE visualization of leukemic clusters in BCR-ABL vs Ph-like samples.........41 Figure 3.11: Phenotypic marker intensity in the 5 most abundant PG leukemic clusters in each sample...................................................................................................................42 Figure 3.12: PG cluster enrichment and abundance analysis revealed significantly different BCR-ABL and Ph-like specific clusters..................................................................................................43

viii

List of Abbreviations

Ab Antibody ABL Abelson tyrosine kinase AML Acute Myeloid Leukemia B-ALL B-cell Acute Lymphoblastic Leukemia BcS Barcode Separation Bcl-xL B cell lymphoma extra-large BCR Breakpoint cluster region BLNK Bruton tyrosine kinase BM Bone marrow BSA Bovine Serum Albumin Cl-casp 3 Cleaved Caspase 3 CML Chronic Myelogenous Leukemia COG Children’s Oncology Group CRLF2 Cytokine receptor-like factor 2 CSF1R Colony Stimulating factor-1 receptor CSM CyTOF Staining Media CyTOF Cytometry by Time-Of-Flight DAS Dasatinib DMSO Dimethylsulfoxide EBF1 Early B cell factor-1 EFS Event-free survival EL Event Length EPOR Erythropoietin receptor ETV6 E26 transformation specific variant 6 FC Fold change FCS Flow cytometry standard FBS Fetal bovine serum FISH Fluorescence in-situ hybridisation gc Common g chain HR High-risk for relapse HSC Hematopoietic stem cell IFN-γ Interferon gamma Ig Immunoglobulin IKZF1 IKAROS Family Zinc Finger 1 IL-3 Interleukin-3 IL-6 Interleukin-6 IL-7Ra Interleukin-7 receptor a chain IMD Integrated mass data ITAM Immunoreceptor tyrosine-based activation motif JAK Janus family kinase KLD Kullback-Leibler Divergence

ix

MAPK Mitogen-activated protein kinase MD Mahalanobis Distance Min Minutes MMI Median metal intensity mTOR Mammalian target of rapamycin OS Overall survival Pax5 Paired Box 5 PBMCs Peripheral blood mononuclear cell PBS Phosphate-buffered saline PCR Polymerase chain reaction Pd Palladium PDGFR Platelet-derived growth factor receptor PG Phenograph Ph Philadelphia chromosome PI3K Phosphoinositide 3-kinase Pre-BCR Pre-B-cell receptor Pre-B Precursor- B Pro-B Progenitor-B PV Pervanadate RT Room temperature RUNX1 Runt-related transcription factor 1 RUX Ruxolitinib SAP Saponin SD Standard deviation SFM Serum-free media SR Standard-risk for relapse SRC Sarcoma kinase STAT Signal-transducer and activator of transcription SYK Spleen tyrosine kinase cRPMI Complete RPMI media TF Transcription factor TKi Tyrosine kinase inhibitor TSLPR Thymic stromal lymphopoietin receptor tSNE t-Distributed Stochastic Neighbor Embedding Tyr Tyrosine VEH Vehicle WBC White blood cell

1

Chapter 1 Introduction

2

1.1 Overview

B cell development is a highly-regulated process involving tightly intertwined transcription

factors (TFs) and signaling pathways. Genetic alterations in these pathways caused by cytogenetic

abnormalities, genes fusions or kinase activating mutations can lead to an arrest in development,

accumulation of immature lymphoid progenitor cells and subsequent B-ALL. Despite being the

most common childhood cancer, B-ALL also affects adults, peaking in prevalence in children

between the ages of 2-5 and subsequently in adults over 501. Along with age and white blood cell

(WBC) count, the genetic alteration is used to classify patients as high-risk (HR) or standard-risk

(SR) for relapse. In pediatric B-ALL, SR subtypes are more prevalent and is one of the reasons

why overall survival rates (OS) are more than 90%2. In contrast, in adult B-ALL HR subtypes are

more prevalent which is one of the reasons why OS rates are below 50%2. This discrepancy in OS

rates highlights the need for better therapies in treatment of HR B-ALL.

The implementation of a tyrosine kinase inhibitor (TKi) specific for the Abelson (ABL)

tyrosine kinase has dramatically increased OS rates in patients whose leukemic blasts harbour the

BCR-ABL (breakpoint cluster region-ABL) fusion. The success of the implementation of this

targeted therapy led to the discovery of a new HR subtype named Ph-like B-ALL in pediatric and

adult B-ALL. Importantly, this subtype is associated with an increase in both prevalence and

morbidity with age. Since the discovery, next generation sequencing techniques have identified a

wide range of kinase activating mutations in 90% of Ph-like cases. 80% of these mutations

converge on either the Janus family kinase/Signal-transducer and activator of transcription

(JAK/STAT) or ABL signaling pathways for which TKis are available and FDA approved for

treatment of other malignancies2.

However, there are currently several impediments in effective clinical implementation of

these TKis into routine Ph-like treatment. First, the kinase activating mutations are highly

heterogeneous so rapid identification with classical techniques is difficult. Second, routine clinical

ALL immunophenotyping methods cannot distinguish Ph-like from other B-ALL subtypes. The

primary goal of my Master’s thesis was to determine if cytometry by time of flight (CyTOF) could

be used to identify Ph-like specific signaling and/or phenotypic marker expression patterns. To

achieve this, I developed and validated a 36-marker CyTOF panel to simultaneously profile

phenotypic and signaling markers in 7 BCR-ABL and 4 Ph-like B-ALL peripheral blood samples

taken at diagnosis. This introduction will explain how disruptions in B cell development can lead

3

to arrest in development and subsequent B-ALL. I will also highlight the pathobiology of the two

genetic subtypes my thesis focused on: BCR-ABL and Ph-like B-ALL. Lastly, I will introduce

CyTOF and explain how it has been used by other groups in Leukemia research.

1.2 B-cell Acute Lymphoblastic Leukemia

B-ALL is a hematopoietic malignancy caused by an accumulation of transformed

progenitor B (pro-B) or precursor B cells (pre-B) in the bone marrow (BM)1-3. This transformation

is caused by a wide range of genetic aberrations including mutations, chromosome translocations,

aneuploidy and gene fusions that alter pathways critical in B cell development1,4. For example,

deletions of IKZF1 (IKAROS Family Zinc Finger 1), the gene essential for lymphopoiesis, are

often observed in B-ALL and are associated with inferior outcomes in pediatric and adult cases2,5-

6. Mutations and fusions involving genes that encode TFs necessary for B-cell commitment and

specification such as E2A, EBF1 (early B cell factor-1) and PAX5 (paired box 5) are also often

present in B-ALL7-9. In normal B cell development, these TFs act upstream and induce expression

of crucial B cell genes including CD79a/b, and enhance V(D)J recombination of the heavy and

lights chains of the B cell receptor10-12. These genes and their respective TFs play a critical role in

controlling normal B cell development. Their disruption results in one of the phenotypic features

observed in B-ALL developmental arrest7.

In addition to genes and TFs, signaling receptors and their downstream pathways, such as

interleukin-7 receptor (IL-7R) and pre-BCR (pre-B cell receptor), are also frequently mutationally

activated in B-ALL. First, both activating and loss-of-function mutations in the IL-7 signaling

pathway have been identified13-14. This pathway plays an important role during the transition from

common lymphoid progenitor to pro-B cells15. Specifically, the IL-7 cytokine binds to the IL-7Ra

and the common g chain (gc) heterodimer which signals through the JAK/STAT pathway

promoting cell survival and proliferation16. Interestingly, the IL-7Ra chain can also pair with the

thymic stromal lymphopoietin receptor (TSLPR) to promote B cell precursor proliferation and

overexpression of TSLPR is often observed in poor outcome B-ALL17. Second, during early B cell

development, pre-B cells are programmed to die unless they successful assemble and signal

through the pre-BCR18. Surprisingly, approximately 70% of B-ALL cases do not express a pre-

BCR on their surface and thus subvert this developmental check point through over activation of

signaling molecules downstream of the pre-BCR or other signaling pathways19-20. Constitutive

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activation of these signaling pathways promotes leukemogenesis through enhanced cell

metabolism, proliferation and impaired apoptosis21. Genetic alterations can sometimes be identified clinically through chromosomal analysis,

polymerase chain reaction (PCR) and fluorescence in-situ hybridisation (FISH)1,22. Along with age

and WBC count, they are used to stratify a patient as HR or SR for relapse23. Patients diagnosed

with SR subtypes are often cured with standard dose chemotherapy, while HR subtypes are more

likely to be resistant. Instead, genetic subtypes classified as HR are treated with high-dose

chemotherapy or bone marrow transplant24-25. In pediatric B-ALL, SR subtypes including

hyperdiploidy (>50 chromosomes), the ETV6-RUNX1 (E26 transformation specific variant 6-runt-

related transcription factor 1) fusion and trisomy 4, 10 and 17 are more prevalent, providing at

least one explanation for high OS rates of ~ 90% for pediatric B-ALL patients4,26-27. OS rates for

adults with B-ALL are significantly inferior (>50%), a statistic that is influenced by a higher

prevalence of HR genetic subtypes. These subtypes include hypodiploidy, the BCR-ABL and the

MLL-AF4 gene fusions4,26-27. In addition to low OS in adults, OS in both pediatric and adult

relapsed and refractory cases are dismal28. These issues highlight a need for better treatment

options or targeted treatment in HR B-ALL.

1.3 BCR-ABL B-ALL

BCR-ABL B-ALL is a HR subtype characterized by expression of the constitutively active

ABL tyrosine kinase29. BCR-ABL gene fusions are often formed by [t(9;22)(q34;q11.2)]

translocations that can be cytogenetically detected as the Philadelphia (Ph) chromosome31. This

translocation fuses the 3’ end of the BCR on chromosome 22 with the 5’ exons of ABL gene on

chromosome 9 to cause constitutive activation of ABL signaling30-32. One phosphorylation target

of the ABL kinase is Tyr177 of the BCR protein, which provides a docking site for proteins that

activate RAS/MAPK and PI3K/AKT signaling34. BCR-ABL also phosphorylates STAT5 in a

JAK-independent manner, and activated phospho-STAT5 (pSTAT5) inhibits programmed cell

death through upregulation of the anti-apoptotic molecule BCL-xL (B-cell lymphoma-extra-

large)33. Thus, signaling through the mutationally activated ABL kinase promotes abnormal

survival, metabolism and proliferation of BCR-ABL leukemic B cells33.

The addition of ABL-targeted TKis to standard of care has dramatically increased OS rates

in pediatric and adult patients with BCR-ABL B-ALL35. Imatinib, the first ABL-targeted TKi

5

increased 5-year OS rates in adults to 43% from the historic 10%36-37. The application of TKi

therapy in BCR-ABL B-ALL provides an excellent example for the potential success of targeted

therapies. However, new therapies are needed for treatment of patients with other HR B-ALL

subtypes, in particular those that lack cytogenetic abnormalities.

1.4 Philadelphia-like B-ALL

The Children’s Oncology Group (COG)-TARGET from St Jude’s Children’s Hospital and

the Dutch Childhood Oncology group performed genome wide expression profiling of pediatric

B-ALL cases in order to better classify and improve treatment options for HR subtypes. They

simultaneously identified Ph-like or BCR-ABL-like B-ALL as a poor outcome subgroup of

pediatric B-ALL38-40. These cases lacked clinically identified translocations including BCR-ABL,

had a high frequency of IKZF1 mutations, and gene expression profiles similar to cases with the

BCR-ABL gene fusion39-40. Since the initial discovery of this subtype in pediatric cases, Ph-like B-

ALL has also been identified in adult B-ALL. The prevalence of this genetic subtype increases

with increasing age, comprising up to 15% of childhood B-ALL, 25% of young adult cases (21-

39 years of age) and up to 33% (>40 years of age) in older adults41-42. Increasing age also correlates

with inferior outcome. For example, the 5-year event-free survival (EFS) rates in young adults is

40.4% compared to 18.9% in older adults42. Nonetheless, across all age groups Ph-like B-ALL is

associated with inferior outcomes compared to non-Ph-like B-ALL41.

More recent studies using next generation sequencing have identified kinase activating

mutations in 90% of Ph-like cases43-44. The largest subgroup has mutations that converge on JAK-

STAT signaling45-46. Specifically, rearrangements in the gene that encodes the TSLP receptor,

CRLF2 (cytokine receptor like factor 2) are the most prevalent alteration in Ph-like, either as a

translocation of CRLF2 to the immunoglobulin heavy-chain transcriptional enhancer region (IGH-

CRLF2) or a deletion of the pseudo autosomal region of the sex chromosomes resulting in a fusion

between CRLF2 and the G-protein purinergic receptor P2RY8 gene (P2RY8-CRLF2). More than

half of these cases also have activating mutations in JAK1 or JAK245-47. Other mutations that

activate JAK-STAT signaling have also been identified in Ph-like B-ALL including gene fusions

involving JAK2, rearrangements in the erythropoietin receptor (EPOR), and a variety of sequence

mutations in JAK1, JAK3, IL7R or SH2B3, a negative regulator of JAK-STAT signaling47-48. The

second major subgroup in Ph-like B-ALL involves rearrangements of ABL-class genes such as

6

ABL1, ABL2, PDGFRB (platelet derived growth factor receptor b) and CSF1R (colony stimulating

factor 1 receptor) 45,47. Many different fusion partners have been identified for each of these genes,

including 12 different partners with ABL1 and 3 with ABL245,47. This genetic landscape suggests

that implementing TKi therapy into standard of care could improve outcomes for patients with Ph-

like ALL.

Pre-clinical and anecdotal reports testing ABL and JAK targeted-TKis in Ph-like ALL have

been promising. These include Dasatinib (DAS), a second generation ABL TKi, and Ruxolitinib

(RUX) JAK 1/2 inhibitor; both FDA approved for treatment of other neoplasms49-51. For example,

in 2013 clinicians reported a successful case of incorporating Imatinib into the treatment of a

refractory pediatric Ph-like B-ALL patient with an EBF1-PDGFRB fusion50. In this case study,

treatment was determined successful as after 14 days of treatment the minimal residual disease in

the patient’s BM was reduced to 0.059%. Furthermore, following 10 months of consolidation

therapy the patient remained in continuous remission50. Based on pre-clinical studies and this case

report, three separate clinical trials (NCT02420717, NCT01406756, NCT03117751) were

launched to test the efficacy of DAS together with standard treatment for Ph-like ABL cases47,51.

RUX has also showed promise in studies using cell lines and patient-derived xenograft models of

CRLF2/JAK mutant Ph-like ALL47,51-52. Furthermore, a completed phase 1 clinical trial

demonstrated the safety of the RUX in refractory pediatric patients51,53. Following completion of

this phase 1 trial, three separate phase 2 trials (NCT02420717, NCT02723994, NCT03117751)

were launched and are ongoing testing the efficacy of RUX in refractory pediatric and young

adults51.

Despite these successes, there are several barriers to implementing TKi for treatment of

Ph-like ALL. First, the genetic alterations observed in Ph-like B-ALL are highly heterogeneous,

which makes it difficult to identify Ph-like cases in a timely manner using classical techniques54.

Second, leukemic sub-clones are often present at diagnosis and relapse highlighting the need for a

single cell diagnostic assay55. Third, routine clinical ALL immunophenotyping methods cannot

distinguish Ph-like from other B-ALL subtypes. Thus, a new single-cell assay is needed to identify

and further classify Ph-like patients in a timely manner.

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1.5 High Dimensional Immune Phenotyping of Leukemia Using Mass Cytometry

Flow cytometry is the most widely used single cell technology for single cell

immunophenotypic profiling of human leukemia. In classical flow cytometry, antibodies (Abs) are

conjugated to fluorophores and molecular expression is quantified based on the fluorophore’s light

emission. However, spectral overlap between fluorophores makes distinguishing them from one

another difficult, especially when the number of markers in the panel is increased. Thus, only a

limited number of markers can be simultaneously profiled using flow cytometry. In contrast, mass

cytometry combines flow cytometry with time-of-flight mass spectrometry to overcome this

limitation. In this technology, known as CyTOF, cells are labelled with Abs tagged with heavy

metal isotopes56, allowing for up to 100 different stable isotopes to be measured with 3-5% overlap

between metal tags57 in contrast to 5-100% overlap between fluorescent tags in conventional flow

cytometry. Currently, at least 40 different metal isotopes are available with sufficient purity to be

routinely used in CyTOF experiments57.

Prior to staining, it is possible to first barcode individual samples with unique metal tags

so that they can later be pooled for multiplexed staining. One barcoding option utilizes six

palladium (Pd) isotopes with atomic masses of 102, 104, 105, 106, 108 and 110. Assigning each

sample a unique 3 Pd isotope code allows for up to 20 samples to be multiplexed stained58.

Multiplex staining has several benefits in CyTOF experiments. First, it reduces staining variability

across tubes. Second, barcoding improves doublet discrimination. In this barcoding scheme, the

combination of any two barcodes, resulting from a cell doublet, yields an “illegal” barcode58. This

scheme cannot detect doublets of cells from the same sample because they have the same 3 Pd

barcode, but every other illegal combination can be detected and removed in de-barcoding58.

However, there are drawbacks to this method. One of the largest issues is that prior to staining

cells are fixed with paraformaldehyde (PFA) and permeablized which can affect surface epitopes

and the quality of staining58. However, overall, Pd based barcoding minimizes sample variation

and allows for up to 20 samples at a time to be multiplexed stained.

In immune profiling CyTOF experiments cells are first incubated with a cocktail of Abs

conjugated to heavy metal isotopes which bind targets of interest on or within the cell. Following

staining, cells are re-suspended with EQ normalization 5 element beads and the solution is

introduced into the CyTOF machine through a capillary tube which delivers the cells to a nebulizer.

In the nebulizer, the addition of argon gas nebulizes the cell suspension into a fine spray of water

8

droplets containing the one or a few cells56-57. The droplets next enter a heated chamber and are

carried into an inductively coupled argon plasma torch that vaporizes, atomizes and ionizes the

cell droplet producing an ion cloud. The cloud next passes through an electrostatic quadrupole ion

deflector which filters out the low molecular mass ions such as oxygen and carbon, leaving an ion

cloud enriched for the heavy metal reporter ions. Finally, the ion cloud, derived from one or a few

cells, is divided into slices that are pushed sequentially into the time of flight (TOF) chamber. In

the TOF chamber, an ion detector measures how long it takes ions to travel the known distance to

the detector. Heavier metals take longer than lighter metals56-57.

After acquisition, software is used to 1) compile sequential “pushes” meeting certain signal

intensity thresholds into cell “events”, 2) convert this integrated mass data (IMD) file into a flow

cytometry standard (FCS) file containing all cell events for that sample and 3) normalize the FCS

file using the global EQ bead “passport” value on the Helios software. Lastly, the FCS is de-

barcoded to produce individual FCS files for each sample in the mixture. These de-barcoded FCS

files can then be analyzed by standard flow cytometry data analysis software to perform manual

gating of one-dimensional histograms or two-dimensional scatter plots56-57. However, this

approach is highly subjective and is limited to examining 2 dimensions at a time. To avoid these

issues, data scientists developed computational workflows that cluster data into cell subsets and

then use dimensionality reduction to visualize the clusters.

Phenograph (PG) is a commonly used algorithm that produces a k-nearest neighbour graph

in which each cell is connected to the k cells most similar to it in high-dimensional space. PG then

uses the Louvain community detection algorithm to find and partition highly interconnected nodes

(groups of cells) into communities or clusters of phenotypically similar cells59,60. These clusters

can then be visualized and explored using the t-Distributed Stochastic Neighbour Embedding (t-

SNE) dimensionality reduction algorithm. tSNE converts the Euclidean distance between two cells

in high dimensional space into a conditional probability (p) matrix. Simultaneously the algorithm

also creates a random conditional probability matrix (q) in 2-dimensional space. The algorithm

then calculates the Kullbach-Liebler divergence (KLD) value to quantify the difference between

the p and q matrices as it iterates through many successive versions of q61. The goal of each

iteration is to decrease the KLD value from the prior iteration. The experimenter chooses the

number of iterations, typically 1000-5000 (depends on total number of events in the run), that

9

produces the lowest final KLD, ideally <5. Together CyTOF and and these algorithms provide an

unbiased and exciting approach to explore the immune phenotypes.

CyTOF has been used by other research groups to highlight the heterogeneity of leukemia

and determine biomarkers of both prognosis and relapse. In 2013, Amir et al., published the first

paper to use tSNE to visualize CyTOF data. In this paper, they compared primary non-leukemic

BM samples to B-ALL BM samples62. Specifically, they applied tSNE to a 29-marker panel

measuring four BM samples, two healthy and two from ALL patients. As expected, the two healthy

samples overlapped in the tSNE graph while the two cancer samples occupied very separate

regions on the tSNE. They also showed preliminary data demonstrating how tSNE can be used to

explore cancer heterogeneity in Acute Myeloid Leukemia (AML) samples. Using a 30 marker

panel and tSNE they showed several markers with gradient expression in two AML samples

including CD33, CD34 and HLA-DR. Lastly, they applied the tSNE algorithm to a matched

diagnostic/relapse pair from one AML patient. The tSNE identified a region of shared phenotypes

in both samples, but rarer at diagnosis suggesting that a rare resistant clone maintained a consistent

phenotype from diagnosis to relapse62.

More recently, the PG and tSNE algorithms were used to examine expression of signaling

and phenotypic markers in AML pediatric samples. Using CyTOF, PG and tSNE this group

identified a new primitive signalling phenotype in AML that predicted survival in an independent

cohort59. In addition, Good et al. incorporated signaling and phenotypic markers to construct a

predictive model of relapse in 60 pediatric BM B-ALL samples with diverse clinical genetics

including 16 patients with JAK2 mutations63. For example, they identified activation of various

signaling molecules including phospho-SYK (Spleen Tyrosine Kinase) and downstream

molecules involved in PI3K/mTOR61 signaling as indicative of relapse. Together, these studies

have demonstrated the value of using high dimensional CyTOF-based single cell profiling of

leukemia.

1.6 Project Rationale and Goals The major goal of my project was to use high-dimensional single cell profiling by CyTOF

to identify signaling pathway activation and/or phenotypic profiles associated with Ph-like B-ALL.

I chose CyTOF as it overcomes several hurdles encountered in fluorescence-based flow cytometry

and allows for simultaneous profiling of currently up to 40 markers. To achieve my goal, I had

10

two specific aims. The first was to develop and optimize a 36 marker CyTOF panel and assay to

measure expression of phosphorylated and phenotypic markers in primary B-ALL samples. The

second was to use this panel and assay to characterize phenotypic marker expression and basal or

TKi treating signaling pathways in Ph-like ALL.

11

Chapter 2 Material and Methods

12

2.1 Cell Culture

Human leukemia or lymphoma cell lines Ramos and U937 were purchased from the

American Type Culture Collection (Manassas, VA, USA), while the K-562 and NALM-6 cell lines

were obtained from Dr. Hans Hitzler (Toronto, ON), and Deutsche Sammlung von

Mikroorganismen und Zellkulturen (DSMZ, Braunschweig, Germany) respectively. Ramos, U937

and NALM-6 cells were cultured in complete RPMI media (cRMPI) comprised of RPMI 1640

GlutaMAX (Thermo Fisher Scientific, Waltham, MA, US) supplemented with 10% fetal bovine

serum (FBS) (Wisent Inc, Saint-Jean Baptiste, QC), 10 mM Hepes (pH 7.2, Wisent Inc), and 1

mM sodium pyruvate (Thermo Fischer Scientific). K-562 cells were cultured in Dulbecco’s

Modified Eagle Medium (DMEM)- GlutaMAX media (Thermo Fisher Scientific) supplemented

with 10% FBS, and 55µM 2-Mercaptoethanol (Thermo Fischer Scientific). The murine pro-B cell

line BaF3 was used in titrations experiment and was obtained from Dr. Robert Rottapel (Toronto,

ON)64. BaF3 cells engineered to express human TSLPR (BaF3-TSLPR) were also used and

obtained from Dr. Shai Izraei (Tel Aviv University, Israel). Both cell lines were cultured in cRPMI

and 0.1-0.5 ng/mL recombinant murine interleukin-3 (IL-3) (PeproTech, Montreal, QC). All cell

lines were cultured in a humidified 5% CO2 incubator. For routine passage, cell lines were

suspended at 5x105 cells/mL in pre-warmed fresh media and transferred to a new vessel. To

determine the number of viable cells, cells were diluted in 0.4% Trypan Blue (Sigma Aldrich,

Oakville, ON) in PBS and counted using a hemocytometer (VWR International, Radnor, PA).

2.2 Peripheral Blood Mononuclear Cells (PBMC) from Non-Leukemic Donors

Single units of whole blood (480 mL of blood and 70 mL of citrate, phosphate, dextrose

anticoagulant) from two different donors were provided by Canadian Blood Services (CBS),

Vancouver, British Columbia and shipped on ice to SickKids Peter Gillgan Centre For Research

and Learning. PBMCs were isolated using Ficoll Paque Plus (GE Life Science) following the

manufacturer’s instructions, viably frozen in 90% FBS and 10% Dimethylsulfoxide (DMSO) and

stored in cryovials in liquid nitrogen until use.

2.3 B-ALL Patient Samples Viably frozen PBMC samples from adult patients with newly diagnosed B-ALL were

obtained from the Princess Margaret Cancer Center (Dr. Mark Minden). These samples were

13

collected after informed consent and following guidelines established by the Research Ethics

Board at the institution.

2.4 Antibodies Details of the Abs used including clone, manufacturer, metal-tag and titration condition

(cell line and treatment) are listed in Tables 2.1 and 2.2. Purified carrier-free Abs were purchased

and metal tagged using Fluidigm Maxpar metal conjugation kits (Fluidigm, Markham, ON)

according to the manufacturer’s instructions. The optimal concentration of each metal-tagged

immunophenotyping antibody was determined by titrating on cell populations containing cells

positive and negative for each marker as described in Results. The optimal concentration of

signaling pathway and apoptosis Abs was determined on cells that were treated with cytokines or

other signaling pathway agonists and/or small molecule signaling pathway antagonists (Table 2.3).

Lyophilized cytokines were dissolved in phosphate buffered saline (PBS, Thermo Fisher

Scientific) supplemented with 1% Bovine Serum Albumin (BSA) (Sigma-Aldrich Canada) at 10

µg/mL, aliquoted and stored at -800C. Goat anti-human IgM (polyclonal) and Staurosporine were

both purchased and stored at 40C until used in experiments. 10 minutes (min) prior to use fresh

Pervanadate (PV) (stock: 100 mM) was made from 480 µL Serum-Free Media ((SFM) RPMI 1640

GlutaMAX, 10 mM Hepes (pH 7.2, Wisent Inc), and 1 mM sodium pyruvate (Thermo Fischer

Scientific), 500 µL 200 mM sodium orthovanadate (Sigma-Aldrich Canada) and 20 µL Hydrogen

Peroxide (VWR International Ltd). 10 µL of this 100 mM PV stock was added per 1 mL of cells

suspended in SFM for a final concentration of 1 mM PV. DAS (Toronto Research Chemicals,

Toronto, ON) and RUX (Cedarlane, Burlington, ON) were reconstituted in DMSO at 30 mM,

aliquoted and stored at -200C. Prior to use, aliquots were further diluted in SFM.

2.5 Cell Staining for Mass Cytometry

Cells were thawed and washed once in cRMPI supplemented with benzonase (Sigma-

Aldrich Canada) and 4mM Mg2+ (Sigma-Aldrich Canada) before resting for 30 min at 370 C in

cRPMI without benzonase and 4mM Mg2+, and lastly deprived of serum through a rest in SFM. B-

ALL samples and control PBMC were serum-starved for 1h whereas cell lines were starved for 2h

(K-562, Ramos, Nalm-6, U937) or 4 hr (BaF3). BaF3 cells were also starved of IL-3 during the 4h

incubation. After serum starvation, samples were collected by centrifugation at 300xg and re-

14

suspended in SFM. Two million cells in 200 µL were aliquoted into 1.2 mL cluster tubes (Fischer

Scientific Company) and warmed for 15 min in a 370C water bath. Signaling agonist or antagonists

were then added in a volume of 50 µL at 5X their final concentration listed in Table 2.1. Cisplatin

(3µM in PBS, Cedarlane) was added for the last 3 min of each treatment. Cells were then

immediately fixed by adding an equal volume of 3.2% paraformaldehyde (Cedarlane). After 10

min at room temperature (RT), samples were washed twice by adding 400 µL of CyTOF staining

media (CSM) (PBS with 1% (w/v) BSA (Thermofisher Scientific)). For all post-fixation washes,

cells were pelleted at 600xg.

Where indicated, samples were barcoded using the Cell-ID 20-Plex Pd Barcoding kit from

Fluidigm, in which 3 different Pd isotopes are chosen from a pool of 6 Pd isotopes to make 20

unique barcodes. The barcoding procedure was carried out according to manufacturer’s

instructions, after which up to 20 samples were pooled into a 15mL Falcon tube for multiplexed

staining. Fc receptors were first blocked by re-suspending cell pellets in 25 µL /3x106 cells of

TruStain (BioLegend, California, USA) for 10 min at RT. Abs specific for surface markers were

diluted to 2X their desired final concentration in CSM. An equal volume of this cocktail was added

to the cells without washing out theTruStain, and cells were incubated for 30 min at RT. Cells

were then washed with 5mL of CSM, and resuspended in 1 mL/3x106 cells of Perm III (BD

Bioscience, Mississauga, ON), a methanol-based permeabilization solution, for 10 min on ice.

Samples were then capped and stored for up to 48h in a -200C freezer.

The next morning cells were brought to RT, washed twice with 5mL CSM and stained with

a cocktail of Abs specific for signaling proteins and other intracellular markers for 30 min at RT.

Cells were then washed twice in 5 mL CSM and re-suspended in PBS containing 0.3% saponin

(SAP), (Sigma-Aldrich Canada, Oakville, ON), 1.6% paraformaldehyde and 0.05 mM 191/193Iridium

(Fluidigm) to stain DNA for up to 48hr at 4oC. In Ab titration experiments, cells were not barcoded

and were stained with the 2- or 3-fold serial dilutions of each Ab ranging from 1/30 to 1/2430. In

titrations for anti-surface marker Abs the cell staining procedure altered slightly. Specifically, cells

were first stained with Cisplatin (1 µM) for 5 min at RT, and immediately quenched with CSM.

Following cells were re-suspended first in 25 µL Fc block for 10 min, and then 25 µL of a 2X

cocktail with titrating abs at concentrations ranging 3-fold from 1/30 to 1/2430.

15

Prior to acquisition on the Helios, cells were washed once with CSM, and once with PBS.

Finally, cells were re-suspended Fluidigm Cell Acquisition Solution containing 5-element EQ

normalization beads (Fluidigm) according to Fluidigm’s protocol. For patient experiments,

between 60,000 and 100,000 events per sample were collected. After acquisition the IMD files

were converted to FCS files. The FCS files were normalized using the global EQ bead “passport”

value on the Helios software.

2.6 De-barcoding and Clustering Analyses

For patient experiments, following normalization, FCS files were concatenated into one

FCS file containing the events collected from all the samples in the experiment. Therefore, prior

to Cytobank analysis the data was first de-barcoded using the single-cell de-barcoder MatLab

software (Nolan Lab, CA, USA). The software assigns each event in the multiplexed concatenated

file to their corresponding sample based on three brightest Pd isotopes present and eliminates

events with less than three Pd stains. The de-barcoder accomplishes this through two calculated

parameters: Barcode Separation (BcS) and Mahalanobis Distance (MD). BcS is a measure of the

intensity difference between the isotopes with the third and fourth highest intensities58,65. This value

is low for debris events which have low intensity values for all Pd isotopes and for cell aggregates

that have high intensity values for four or more Pd isotopes58,65. Increasing the stringency or value

of the BcS filters out these unwanted events. MD quantifies the distance, in Pd intensity space,

between an event and the distribution of its assigned barcode population. Application of a lower

MD value removes events with inconsistent Pd intensities58,65. These two parameters were used

accordingly in experiments to eliminate unassigned events and de-barcode samples. After de-

barcoding the MatLab software produces new FCS files, one per each sample/treatment.

The de-barcoded FCS files were then uploaded to the cloud-based storage and analysis

software Cytobank (Santa Clara, CA, USA). For titration experiments, FCS files were uploaded

directly to Cytobank. For all experiments, Cytobank was used to perform pre-gating to remove

EQ beads, debris, aggregates and dead cells, as well as to perform additional gating and

visualization for statistical analyses of titration experiments.

For the patient experiments I used the open source PG algorithm

(github.com/jacoblevine/PhenoGraph) to identify cell subsets in an unbiased manner. I performed

the clustering on FCS files containing only single live cells (exported from Cytobank) from 11

16

leukemic samples (7 BCR-ABL, 4 Ph-like) and 3 non-leukemic PBMC controls (244,986 events

total). The user-defined parameters were k=30 and Arcsinh scale argument=5, and I clustered

using the following 12 markers: CD45, HLA-DR, TSLPR, CD34, CD19, CD14, CD127, CD38,

CD33, CD3, CD79a and IgM. The event count from each sample was down-sampled to 17,499,

the lowest event count across all the samples. The R package “flowCore” was used to create new

FCS files which included PG cluster IDs. The FCS files were uploaded to Cytobank and run

through the dimensionality reduction algorithm tSNE using the same phenotypic markers as in

clustering and the PG cluster IDs. tSNE analysis was run with the following settings:

iterations=3,000, theta = 0.5, perplexity= 50. The final KLD value was 4.3. The 2-dimensional

plots produced by tSNE were then coloured based on PG clusters or markers of interest. To

visualize and quantify the PG clusters, I used the automatic clustering feature in Cytobank.

A matrix of Median Metal Intensities (MMIs) of phenotypic markers (CD45, CD34,

CD79a, CD19, IgM, HLA-DR, CD38, CD14, CD33, CD3, CD127 and TSLPR) from the leukemic

population (CD45lo CD34all) was imported into R and the heatmap.2 function from the R package

‘gplots’ was used to plot heatmap(s). Heatmap.2 function also carried out hierarchical clustering

of samples (results represented by column dendrogram) and of markers (resulted represented by

row dendrogram). MMIs are represented as SD from row mean (z-scores) for visualization

purposes, calculated using the heatmap.2 function66-67.

2.7 Statistical Analysis Agonist/Antagonist potentiated phospho-protein levels were normalized to phospho-

protein levels in untreated cells using the Log2 fold-change (FC) ratio calculated in Cytobank. This

ratio is the Log2 (MMI of the marker in treated cells divided by the MMI of the marker in untreated

cells). Log2 FC ratios and basal pSTAT5 MMIs were compared between subgroups with two-tailed

unpaired T-tests. MMIs of pAbs from VEH and DAS treated BCR-ABL samples were also

compared with two-tailed ratio paired T-tests. Cluster enrichments were compared Fisher’s exact

test. Multiple t-tests were also used to compare cluster abundance and MMIs of subgroup specific

clusters. The original FDR method of Benjamini and Hochberg method was used with a desired

FDR (Q) of 5%. For all analysis threshold for statistical significance was set below P=0.05. All

analysis and graphs displayed were made in Prism GraphPad Software (V8.0, La Jolla, California).

17

Table 2.1 Immunophenotyping Antibodies

Protein Name Clone Manufacturer Metal-Tag Titration context

CD45 Hl30 BioLegend 89Y Ramos+BaF3 cells HLA-DR L243 BioLegend 141Pr PBMCs TSLPR 1B4 BioLegend 145Nd BaF3-TSLPR+Ramos CD34 581 BioLegend 148Nd Cord blood MNCs CD14 M5E2 BioLegend 160Gd PBMCs CD127 eBioRDR5 eBioscience 165Ho PBMC CD19 HIB19 BioLegend 168Er PBMCs CD33 WM53 BioLegend 169Tm PBMCs CD38 HIT2 BioLegend 172Yb PBMCs CD3 UCHT1 eBioscience 174Yb PBMCs

CD79a HM47 BD Biosciences 173Yb PBMCs

18

Table 2.2 Antibodies Specific for Signaling and Apoptosis Markers

Protein Name Clone Manufacturer Metal-Tag

Titration context

Cl Casp 3 C92-605 BD Biosciences 142Nd NALM-6 +/- Staurosporine

pCRKL (Y207) K30-391.50.80 BD Biosciences 143Nd Ramos +/- PV

pTyr pY20 Invitrogen 144Nd Ramos +/- IgM pEIF4E (S209) J77-925 BD Biosciences 146Nd BaF3 +/- IL-3

pJAK2 (Y1007/Y1008) C80C3 Cell Signaling Tech 147Sm BaF3 +/- IL-3

p4EBP1 (T36/T45) M31-16 BD Biosciences 149Sm Ramos +/- PV pSTAT5 (Y694) 47 BD Biosciences 151Eu BaF3 +/- IL-3

pAKT (S473) M89-61 BD Biosciences 152Sm BaF3 +/- IL-3 pSTAT1 (Y701) 4a BD Biosciences 153Eu U937 +/- IFN-g

pSRC (Y418) K98-37 BD PhosFlow 155Gd Ramos +/- IgM pp38 (T180/Y182) 36 BD Biosciences 156Gd Ramos +/- PV pSTAT3 (Y705) 4/P BD Biosciences 158Gd BaF3 +/- IL-3 pBLNK (Y84) J117-1278 Pharmingen 162Dy Ramos +/- IgM

pPLCg2 (Y759) K86-689.37 BD Biosciences 163Dy Ramos +/- IgM IkBa L35A5 Cell Signaling Tech 164Dy Ramos +/- PV

pNFkB (S529) K10-895.12.50 BD Biosciences 166Er PBMC +/- PMA

pERK 1/2(T202/Y204) 20a BD Biosciences 167Er Ramos +/- IgM, DAS

pBTK (Y223) N35-86 BD Biosciences 170Er Ramos +/- IgM pSYK (Y525/Y526) C87C1 Cell Signaling Tech 171Yb Ramos +/- IgM

pS6 (S235/S236) N7-548 BD Biosciences 175Lu PBMCs +/- PMA pMTOR (S2448) O21-404 BD Biosciences 176Yb BaF3 +/- IL-3

19

Table 2.3 Signaling Agonists and Antagonists

Name Agonist or Antagonist

Concentration Treatment Time

Manufacturer

Goat F(ab’) Anti-Human IgM

(Polyclonal) Agonist

10 µg/mL 8 min Southern Biotech (Birmingham, AL)

Interleukin-3 (IL-3) Agonist

10 ng/mL 15 min Stem Cell Technologies (Vancouver, BC)

Interferon gamma (IFN-γ) Agonist

10 ng/mL 10 min ThermoFisher (Waltham, MA)

Staurosporine Antagonist (PKC)

1 µM 1 hr Sigma-Aldrich Canada (Oakville, ON)

Phorbol 12-myristate 13-

acetate (PMA) Agonist 50 ng/mL Overnight

(O/N) Sigma-Aldrich Canada

(Oakville, ON)

Ionomyocin (Ion) Agonist 500 ng/mL O/N Sigma-Aldrich Canada

(Oakville, ON)

Pervanadate (PV) Agonist 1 mM 20 min Sigma-Aldrich Canada

(Oakville, ON)

Dasatinib (Das) Antagonist (SRC, ABL

TKi)

30 nM 30 min Toronto Research Chemicals

(Toronto, ON)

Ruxoltinib (RUX) Antagnoist (JAK1/2

TKi)

300 nM 30 min Cedarlane (Burlington, ON)

20

Chapter 3

Results

21

3.1 Optimization and Validation of CyTOF Antibodies

To simultaneously profile phenotypic and signaling markers a large panel of over 35

markers is needed. Building a panel of this size is difficult in fluorescence-based flow cytometry

due to spectral overlap and compensation. CyTOF overcomes these limitations by using a mass

spectrometer coupled to a flow cytometer and quantifies epitopes using Abs conjugated to heavy

metals instead of fluorophores. The use of heavy metal isotopes as reporters allows for

simultaneous profiling of up to 40 markers at a time56. Therefore, my goal was to build and

optimize a CyTOF panel and assay to profile signaling and phenotypic markers in B-ALL samples.

Specifically, I designed and validated a 36-marker CyTOF panel, seen in Fig 3.1A to

identify key cell lineages and signaling pathway activation in viably frozen diagnostic B-ALL

samples. I included Abs specific for various B cell markers including CD19 and intracellular

CD79a. I also included Abs specific for HLA-DR, CD38 and intracellular IgM to classify B cell

differentiation states. Leukemic B cells typically express lower amounts of CD45, a pan-

hematopoietic marker, than normal B cells. They also are often positive for CD34, a

stem/progenitor cell marker23. Therefore, I also included Abs specific for CD45 and CD34. As

approximately 50% of Ph-like samples aberrantly express the TSLPR heterodimer (TSLPR + IL-

7Ra (CD127)), I included Abs specific for both CD127 and TSLPR68. To identify myeloid and T

cells, I added Abs specific for CD3, CD33 and CD14 Abs to the panel. To detect signaling activity,

I also incorporated Abs specific for pSTAT5 a known ABL and JAK target, and various

phosphorylated signaling proteins in the PI3K/mTOR, RAS/MAPK and Pre-BCR signaling

pathways. Lastly, I included an Ab specific for Cleaved caspase-3 (Cl-casp 3) to identify and

exclude pre-apoptotic cells as they signal differently from live cells. I first validated and titrated

each Ab to their optimal dilution on cell lines or PBMC samples known to include positive and

negative cells for each marker.

For the Ab titration examples shown in Fig1 and all data shown in this thesis I used the

gating strategy in Fig 3.1B. First, I excluded EQ beads and debris by gating on EQ beads(140Ce)-

DNA+ events. From this “Cell” population (EQ-DNA+) I excluded aggregates using 2D plots of the

Event Length (EL) vs Center and EL vs Offset Gaussian Distribution parameters. EL is the number

of detector scans it takes to measure an ion cloud. A typical single cell occupies 10-40 scans of the

detector. Therefore, ion clouds with a higher EL are likely doublets and were eliminated. Center

and Offset are Gaussian parameters derived from the Helios acquisition software and are used to

22

identify outliers69. These two gates identified a “Single Cell” population. Lastly, from this “Single

Cell” population I removed dead and pre-apoptotic cells stained for Cisplatin or Cl-casp 3. All

plots shown were gated on the “Live Single Cell” population created by this 4-gate combination.

To titrate Abs specific for phenotypic markers I stained samples known to include cells

positive and negative for the marker of interest (eg., PBMC or tonsil samples). Alternatively, I

mixed cell lines known to be positive or negative for the marker. The titration condition I used for

each phenotypic marker is listed in Table 2.1 in the Material and Methods section. I choose the

optimal dilution by calculating the staining index (SI) using the formula shown in Fig. 3.1C. This

SI ratio accounts not only for the difference in intensity of the positive cells, but also for variation

in the spread of the negative population. For example, to titrate the TSLPR Ab I mixed TSLPR-

human CD19+ Ramos B lymphoma cells with murine CD19- BaF3 cells transfected with human

TSLPR. I stained this cell mixture for 30 min with an Ab-cocktail including anti-CD19 at a

constant dilution and anti-TSLPR at the following dilutions: 1/30, 1/90, 1/270, 1/810 and 1/2430.

Ultimately, I chose 1/300 as the optimal dilution since the SI ratio decreased at higher dilutions

To titrate phospho-specific Abs, I compared staining of untreated cells to those treated with

signaling pathway agonists or antagonists in cell lines. The titration conditions I used for phospho-

specific Abs are listed in Table 2.2 of my Material and Methods section. In the example shown in

Fig 3.1D I treated U937 cells with interleukin-6 (IL-6) for 15 min prior to fixation,

permeabilization and staining with anti-pSTAT3 at the following dilutions: 1/30, 1/90, 1/270,

1/810 and 1/2430. Ultimately, I chose 1/100 because at lower concentrations the pSTAT3 MMI of

the untreated cells decreased sharply and a large fraction of IL6-stimulated cells no longer stained

as reflected in the increasing Log2 FC ratios.

Surface markers Intracellular markers

EQ B

eads

–14

0Ce

DNA1 (Iridium) Center

Even

t Len

gth

Offset

Even

t Len

gth

Viability (Cisplatin)

Clea

ved

casp

ase 3

16.3 16.6 17.4 14.7 10.4

CBaf3-TSLPR+ Ramos

1/30 1/90 1/270 1/810 1/2430

TSLPR

CD

19

MMIpos- MMIneg

2XSDneg

A B

1/30 1/270

VEHIL-6

1/8101/90

Log2 FC (IL6/VEH)

U937

2.3 2.6 4.1 7.1

-7.05 7.053.52-3.52 0

D

Log2 MMI IL-6 treatedMMI untreated

pSTAT3

Cells

Cells Single Cells

Single Cells

23

Fig 3.1

24

Figure 3.1: Development and optimization of a 36-marker CyTOF panel. (A) CyTOF panel

separated into surface (12) and intracellular (24) markers. (B) Example of pre-gating strategy

shown for all experiments. The cells used in this example were non-leukemic PBMCs. Contour

plots of EQ beads (140Ce) vs DNA Intercalator Iridium were first gated to identify “Cells” and

exclude beads and debris. Plots of Event Length vs Offset and Event Length vs Centre gated on

the “Cells” population were then used to exclude doublets and identify “Single Cells”. Finally

plots of Cleaved Caspase-3 vs the Cisplatin gated on the “Single Cell” population were used to

exclude dead and pre-apoptotic cells. (C) Example of a titration for a surface marker Ab. A 50/50

mixture of BaF3 cells transfected with human TSLPR and Ramos cells were stained with anti-

CD19 (constant dilution) and 3-fold serial dilutions of anti-TSLPR ranging 1/30 to 1/2430.

Contour plots show CD19 vs TSLPR on the live single cell population defined using the pre-gating

strategy shown in (A). Gates used to identify the positive (CD19- TSLPR+) and negative (CD19+

TSLPR-) populations used for the SI calculations are shown. This calculation yields the difference

between the MMI of positive cells and negative cells divided by 2x the standard deviation (SD) of

the negative cells. (D) Example of a titration for a signaling Ab. U937 cells were stimulated with

SFM (Vehicle, VEH) or IL-6 for 15 min prior to fixation, permeabilization and staining with 3-

fold serial dilutions of anti-pSTAT3. Histograms show pSTAT3 intensity in VEH-treated and IL-

6 treated cells at each Ab dilution. To determine the optimal dilution for each Ab I calculated the

Log2 FC ratio of the MMI of treated cells divided by the MMI of cells treated with SFM (VEH).

25

3.2 Validation Pilot on BCR-ABL Samples

Before profiling Ph-like samples, I wanted to ensure that the assay worked efficiently with

patient samples and that I could detect activated signaling in leukemic blasts. To achieve this goal,

I completed a pilot experiment on 3 BCR-ABL samples, the BCR-ABL CML (chronic myeloid

leukemia) cell line K-562 (a positive control for ABL signaling) and PBMCs from a non-leukemic

donor +/- the ABL-specific TKi DAS or PV (Fig 3.2). I included PV to inhibit phosphatase activity

and globally activate signaling to verify signaling-induced changes in phospho-Ab staining in each

sample. I chose BCR-ABL samples first as they have activated ABL signaling and are more readily

available compared to Ph-like samples. Following thaw and serum-starve, I divided each sample

into three cluster tubes and treated with SFM (VEH), DAS or PV. After treatment, I barcoded each

sample with a unique combination of 3 Pd isotopes from a pool of 6 (known as a 6 choose 3

combinatorial strategy) and pooled the samples together for staining. Ab cocktails specific for

surface and phosphorylated or intracellular markers were used sequentially as described in

Materials and Methods.

After pre-gating, I identified notable differences in expression of several markers in PBMC

from leukemic and non-leukemic donors. PBMC from non-leukemic donors contained CD45hi

subsets expressing CD19 (B cells), CD33 (myeloid cells) and CD3 (T cells) (Fig 3.3). All leukemic

samples also contained a discrete T cell population but had few CD45hi cells that co-expressed

CD19 or CD33. PBMC from non-leukemic donors also contained rare CD45lo CD34+ cells, likely

circulating hematopoietic stem cells and progenitors. By contrast, PBMCs from BCR-ABL

leukemic donors had a prevalent CD45lo CD34+/- CD19+ population that expressed surprisingly

varying levels of the other markers. These data show that my CyTOF panel can readily identify

leukemic B cells in BCR-ABL samples.

As the BCR-ABL gene fusion is known to activate numerous signaling pathways, I next

investigated the signaling patterns in these samples. The histograms in Fig 3.4A display basal

phosphorylation of various markers and the effects of DAS and PV in leukemic cells of 2 BCR-

ABL samples. Importantly, PV treatment increased expression of all phospho-markers,

demonstrating that the Abs can detect signaling-related changes in phosphorylation and that

leukemic blasts from the patient samples were capable of signaling. DAS treatment significantly

decreased pSTAT5 in the BCR-ABL B-ALL samples and robustly in the K-562 cells (Fig 3.4B/C),

demonstrating that in both STAT5 phosphorylation is ABL dependent. The BCR-ABL samples

26

showed varying basal expression of other phospho-markers including pSYK and pSRC (phospho-

Sarcoma kinase) that were also significantly decreased by DAS treatment. In conclusion, this pilot

experiment showed that this assay can detect oncogenically activated signaling in leukemic patient

samples.

Pd BC1-1312 Phenotypic Markers

20 Signaling & Intracellular Markers

De-barcode & Analysis

SamplesBC1 BC2 BC3BC4 BC5 BC6

BC13

BC7 BC8 BC9

Patient 1

Patient 2

K-562PBMCs

VEHDASPV

Patient 3BC10 BC11 BC12

Treatment

3 BCR-ABL PBMC5 samples 1 Non-leukemic PBMC

K-562 cell line

Each sample

Cluster tubes for treatment

1. Thaw2. 30 min rest at 370C

3. 1h serum-starve

27

Fig 3.2

CD

45

CD34 CD19CD3 HLA-DRCD38

795

100709

90124

CD33

Non-leukemicPBMC

BCR-ABL B-ALL

“Leukemic Cells”

28

Fig 3.3

BCR-ABLB-ALL795

90124

pSTAT5 pSYK pSRC pBTK

VEH

DAS

PV

VEH

DAS

PV

Log2 FC (DAS/VEH or PV/VEH)

-7.0 7.00-3.5 3.5

A

C

Log2 FC (DAS/VEH)

DAS

VEH

-2.92 2.921.46-1.46 0

BCR-ABL CML Cell Line

K-562

pSTAT5

29

pSTAT5P=0.01

P=0.02

pSYK

P=0.0004

pSRC

Treatment

BFig 3.4

30

Figure 3.2: Pilot CyTOF analysis of BCR-ABL samples. Top: Schematic overview of sample

preparation. PBMCs from patients with BCR-ABL B-ALL or non-leukemic donors were thawed,

rested for 30 min at 370C and serum-starved for 1h in SFM. K-562s were harvested and serum-

starved for 2h. All cells were then transferred to cluster tubes. Bottom: Schematic of cell treatments

(VEH, DAS (30nM) for 30 min or PV for 20 min). Cisplatin (3µM) was added for the final 3 min

on treatment. Cells were then fixed, permeabilized and barcoded with their own unique 3 Pd

isotope code. Following barcoding, all samples were transferred to a 15 mL falcon tube, stained

with a cocktail of antibodies specific for surface markers, permeabilized with methanol-based

Perm III solution and stained with a cocktail of Abs specific for phosphorylated/intracellular

markers. Samples were then re-suspended in the DNA intercalator Iridium, stored overnight and

run on the Helios machine the following morning. The Helios software was used for pre-processing

to generate and normalize FCS data files. The files were then de-barcoded and uploaded to

Cytobank for analysis.

Figure 3.3: Highly heterogeneous marker expression in BCR-ABL samples. Contour plots

were of live single cells show CD45 (y-axis) vs the indicated markers on the x-axis. PBMCs from

non-leukemic donor CBS2 and three patients with BCR-ABL B-ALL are separated by row. Leukemic cells (CD45lo CD34all) were identified by the gate in the first column. T (CD45hi CD3+),

B (CD45hi CD19+) and myeloid cells (CD45hi CD33+) are identified by gates in the non-leukemic

PBMC control, and T-cells (CD45hi CD3+) are identified in each leukemic sample.

Figure 3.4: Signaling pathway activation in BCR-ABL Samples. (A) Impact of DAS or PV

treatment in 2 representative BCR-ABL samples. Histograms show the indicated markers gated on

the live single leukemic cell population identified in Figure 3.2 Histograms were coloured based

on the Log2 FC Ratios of DAS/VEH or PV/VEH and were scaled globally. (B) Impact of DAS

treatment on pSTAT5, pSYK and pSRC in leukemic cells. Plots compare the MMIs of pSTAT5,

pSYK and pSRC in VEH-treated BCR-ABL samples (n=3) to the MMIs in DAS-treated samples

(n=3). Two-tailed P values from ratio paired T-tests are shown. (C) Example of effect of DAS

treatment of pSTAT5 in the CML cell line K-562. The histograms were displayed as described in

Fig 3.4A.

31

3.3 CyTOF Profiling of Signaling Activity in Ph-like vs BCR-ABL B-ALL Subgroups

Most Ph-like ALL cases have mutations that activate JAK/STAT or ABL signaling.

Therefore, I used my CyTOF assay to compare signaling activity and phenotypic markers in

PBMC from adult patients with Ph-like (n=4) compared to BCR-ABL (n=7) B-ALL over three

experimental days as laid out in Fig 3.5. I included PBMC from a non-leukemic donor in each

experiment as a positive control for the phenotypic markers. Among the 4 Ph-like cases, one was

CRLF2- with a PAX5-JAK2 fusion, two were CRLF2+, and one had a Ph-like gene expression

signature but signaling mutations associated with this class were not detected by RNA-sequencing

(Mark Gower and C. Guidos, unpublished data).

Two-dimensional contour plots of CD34 versus selected phospho-markers for 3 samples

from each group are shown in Fig 3.6A. The CD34+ leukemic cells in BCR-ABL samples exhibited

high pSTAT5, as expected based on prior reports that pSTAT5 is a direct ABL target as well as

high pSYK and pMTOR33,70. Interestingly, pSTAT5 was more highly expressed by CD34+

leukemic cells in the Ph-like sample with the PAX5-JAK2 fusion than in the CRLF2+ cases. In fact,

basal pSTAT5 levels were significantly higher in the samples with ABL or JAK2 mutations

compared to the samples without (Fig 3.6B). Importantly, DAS decreased pSTAT5 more robustly

in the BCR-ABL than the Ph-like samples, indicating the dependence of STAT5 phosphorylation

on active ABL signaling in this group (Fig 3.6C). Lastly, RUX decreased pSTAT5 more robustly

than DAS in all 3 of these Ph-like CRLF2 and JAK2 samples (Fig 3.6C). These data suggest that

this CyTOF phospho-assay can identify leukemic blasts with activating ABL or JAK2 mutations

based on their higher expression of basal pSTAT5 and its reduction by ABL- or JAK2-selective

tyrosine kinase inhibitors

Sample ID Key Mutations Leukemic Subgroup100709 BCR-ABL BCR-ABL795 BCR-ABL BCR-ABL90124 BCR-ABL BCR-ABLCBS2 (non-leukemic) -110833 BCR-ABL BCR-ABL120324 BCR-ABL BCR-ABL100513 PAX5-JAK2 Ph-like JAK2260153 CRLF2+ Ph-like CRLF2827 BCR-ABL BCR-ABLCBS2 (non-leukemic) -285035 CRLF2+ Ph-like CRLF2100702 ERG-LINC, IKZF1 Ph-like Other9407 BCR-ABL BCR-ABLCBS2 (non-leukemic) -

LeukemiaSubgroup

# of Sample(s)

BCR-ABL 7

Ph-like JAK2 1

Ph-like CRLF2 2

Ph-like Other 1

CBS2 (non-leukemic) 3

Summary Table

Pilot

Exp #1

Exp #2

Exp #3

A B

32

Fig 3.5

pSTAT5 pSYK pMTOR

CD

34795

827

110833

A B

100513

260153

285035

BCR-ABL

Ph-like CRLF2/JAK2 P= 0.02

P= 0.04C

P= 0.01

pSTAT50

10

20

30

40

50

ABL or JAK2 mutationsNo ABL or JAK2 mutations

MM

I

pSTAT5-3

-2

-1

0

Log 2 F

C (D

AS/

VEH

)

Marker

BCR-ABL+

Ph-like CRLF2/JAK2

33

Fig 3.6

34

Figure 3.5: Patient cohort for Ph-like vs BCR-ABL experiments. (A) List of sample IDs, key

mutations and subgroups profiled in the BCR-ABL pilot and 3 additional experiments. All samples

are from adult patients at Princess Margaret Cancer Centre. PBMC samples from a single non-

leukemic donor were used (CBS2). The clinical laboratory at Princess Margaret Cancer Centre

identified all BCR-ABL samples as such by cytogenetic analysis, FISH or RT-PCR. The clinic

laboratory also identified the Ph-like CRLF2 samples (260153 and 285035) as CRLF2+ by flow

cytometry. The PAX5-JAK2 fusion in sample 100513 was identified by my fellow student Mark

Gower using RNA-sequencing. Sample 100702 was classified as Ph-like Other based on gene

expression profiling performed by Mark Gower. (B) Summary table of number of samples profiled

per genetic subtype. In experiment 3 and 4 all samples were also treated with RUX (300 nM) for

30 min.

Fig 3.6: DAS and RUX decrease pSTAT5 more robustly in BCR-ABL and Ph-like

CRLF2/JAK2 samples respectively. (A) Comparison of basal pSTAT5, pSYK and pMTOR

levels among BCR-ABL, Ph-like CRLF2 and Ph-like JAK2 samples. Contour plots of live single

cells from untreated are shown for 3 representative BCR-ABL, 1 Ph-like JAK2 (Sample ID in dark

blue) and 2 Ph-like CRLF2 (sample ID in light blue) are shown. Plots show CD34 (y-axis) vs the

indicated markers on the x-axis. The percentage of cells in each quadrant is shown. (B) Box and

whisker plots show basal pSTAT5 MMI of leukemic cells from untreated samples with mutations

in ABL or JAK2 (BCR-ABL + Ph-like JAK2, n=8) compared to those without mutations in ABL or

JAK2 (Ph-like CRLF2 + Ph-like Other HR, n=3). Each dot shows the pSTAT5 MMI from a single

sample and the line is the mean MMI of all samples. (C) Effect of DAS or RUX treatment on

pSTAT5 in BCR-ABL vs Ph-like CRLF2/JAK2 (Ph-like CRLF2 + Ph-like JAK2) samples. Violin

plots compare the Log2 FC ratio of Treated/Untreated (DAS/VEH or RUX/VEH) cells for pSTAT5

between groups. DAS treatment: BCR-ABL n=7, Ph-like CRF2/JAK2 n=3. RUX treatment: BCR-

ABL n=2, Ph-like CRLF2/JAK2 n=3. P values from unpaired, two-tailed T-tests are displayed for

comparison in B and C.

35

3.4 Unsupervised Clustering Algorithm Reveals Differential Expression of Phenotypic

Markers in Ph-like and BCR-ABL Samples I next sought to determine if these Ph-like and BCR-ABL samples exhibited distinct

phenotypic marker profiles, since this could be helpful in identifying Ph-like samples in the clinic.

To identify any group specific patterns, my fellow student Mark Gower performed hierarchical

clustering of the MMIs of phenotypic markers from leukemic cells in each sample. However, the

dendrogram did not reveal any group-specific patterns (Fig 3.7). Therefore, I next used PG, an

unsupervised clustering algorithm to ask whether BCR-ABL and Ph-like samples contained distinct

cell subsets. Lastly, I used the dimensionality reduction algorithm tSNE to visualize the PG

clusters.

In my PG analysis, I chose K=30 and clustered 17,499 events (244,986 events total) from

each of the 7 BCR-ABL, 4 Ph-like and 3 non-leukemic PBMC samples using 12 phenotypic

markers: CD45, CD34, IgM, CD19, CD79a, CD38, HL-ADR, CD33, CD14, TSLPR, CD127 and

CD3. Expression of the signaling markers was generally low and none differed significantly across

the 2 subgroups (data not shown). Therefore, they were not considered further in my analysis.

The major cell lineages cluster distinctly and mapped to different areas on the t-SNE maps

from one another, as seen in the representative plots from one control PBMC sample, coloured by

expression of CD3, IgM or CD33 (Fig 3.8A). To further explore the non-leukemic clusters, I

graphed the abundance of each cluster as the % of total live population in the control PBMC

samples (Fig. 3.8B). From these graphs, I identified the top 8 most abundant clusters in non-

leukemic PBMC control as 9,13,14, 15, 16, 17, 20 and 23. As depicted in the heat map in Fig 3.8C,

cells in the top 2 most abundant clusters expressed myeloid or B cell markers, while the 3rd to 8th

most abundant clusters expressed T cell markers. PG identified 8 major non-leukemic clusters

which represented B, myeloid and T-cells.

These 8 non-leukemic clusters were also present to varying extents in the leukemic samples

as shown by the t-SNE maps in Fig 3.9. For example, in sample 9407 the T cell clusters (top right,

coloured orange-red) were much less abundant than in the non-leukemic PBMC example above it.

In contrast, in other samples such as 285035, the T cell clusters appeared very similar in abundance

to the PBMC control. By contrast, inspection of the tSNE maps for each leukemic sample showed

the presence of unique clusters that were not found in the PBMC controls.

36

Examination of t-SNE maps that were pre-gated on CD45lo CD34+/- cells from each sample

confirmed that these clusters were leukemia-specific (Fig 3.10) and revealed differences in the

leukemic clusters from BCR-ABL versus Ph-like subgroups. For example, the leukemic clusters

from Ph-like samples, irrespective of their mutation, occupied the right side of the t-SNE maps,

whereas they occupied the left side of the maps in 5/7 of the BCR-ABL samples. The two BCR-

ABL samples (9407 and 827) that did not follow this pattern mapped more similarly to Ph-like

samples. Interestingly, these two samples also clustered with the Ph-like samples in the

dendrogram in Fig 3.7.

These data suggested that the prevalence of some leukemic clusters differed across BCR-

ABL vs Ph-like samples. To further explore this idea, I used heatmaps to examine marker intensity

among the leukemic clusters in each sample (Fig 3.11). I chose to examine the top 5 most abundant

clusters since they accounted for > 60% of the total live singlet population across 10/11 samples

analyzed. The only exception was sample 827 in which the top 5 clusters totaled 37 % of the total

cells. Interestingly, the top 5 most abundant leukemic clusters and their marker intensities were

quite heterogeneous both within and between the two subgroups. For example, the 5/7 BCR-ABL

samples had a CD14-expressing cluster that was absent from the Ph-like samples. Overall, the 5

most abundant leukemic clusters exhibited highly variable expression of phenotypic markers in

both the BCR-ABL and Ph-like subgroups.

I next asked if the two leukemia subtypes exhibited differential cluster enrichment or

cluster abundance within the top 5 most abundant leukemic clusters. First, to determine which

clusters were enriched in the both subgroups, I constructed contingency tables and used Fisher’s

exact test to determine if the number of cases that had each PG cluster in their top 5 most abundant

clusters differed by leukemic subgroups (Fig 3.12A). Second, I compared the abundance of cells

in selected clusters across the two subgroups (Fig 3.12B). This analysis revealed that cluster 0 was

not selectively enriched in one group over the other. Moreover, although cluster 0 trended towards

higher abundance in the Ph-like samples, this difference was not significant. However, other

clusters were differentially represented in the 2 leukemia groups. In particular, cluster 5 was

significantly enriched in BCR-ABL samples, whereas cluster 8 was significantly enriched in Ph-

like samples. These 2 clusters also showed significantly different abundance between the 2 groups.

Cluster 2 was also present in 5/7 BCR-ABL samples and not in Ph-like samples, and thus was more

37

highly enriched in BCR-ABL samples. Clusters 4 and 6, although not enriched, were significantly

more abundant in the Ph-like samples.

To characterize phenotypic differences between these differentially enriched or abundant

clusters, I compared the mean MMI of phenotypic markers from the 7 BCR-ABL samples in BCR-

ABL specific clusters to the MMIs from the 4 Ph-like samples in Ph-like specific clusters. I found

that several markers were differentially expressed in the class-distinguishing clusters 5 and 8.

Interestingly, cluster 5, enriched in BCR-ABL samples, expressed the primitive marker CD34

together with B cell (CD19, CD79a) and the monocyte marker CD14. Except for CD14, these

markers were also expressed by cluster 8, enriched in Ph-like samples. However, CD34 and CD19

were significantly (p>0.05) higher in cluster 5. In contrast, CD127 and HLA-DR were significantly

higher in cluster 8. The BCR-ABL-associated cluster 2 also had significantly higher CD14

expression than cells in the Ph-like associated clusters 4 and 6. These data suggest that cell subsets

with differing expression of CD34, CD19, CD14, CD127 and HLA-DR contributed to the distinct

clustering of Ph-like and BCR-ABL samples.

-2

20 1-1Row Z-Score

795

1203

24

9012

4

1108

33

1007

09

1005

13

9407827

2601

53

2850

35

1007

02

CD79aCD38HLA-DRCD45CD19CD34

CD14CD33IgM

CD127TSLPR

Colour Key

Row Z-Score-2 -1 0 1 2

38

Fig 3.7

tSNE1

CD3 CD33

T Cells B Cells Myeloid CellsA

tSNE2

Myeloid CellsC CD3

IgM

9

13

14

15

16

17

20

23

CD79a

CD19

CD14CD33CD38

B Cells

T Cells

Cluster

Arcsinh Ratio of Medians

-6.37 6.370-3.17 3.17

B1 2 3PBMC30

20

10

00 32

% o

f tot

al

Cluster 0 32 0

HLA-DR

IgM

39

Fig 3.8

CD127

32

CBS2

100709 795 9407

Ph-like

100513 260153 285035

BCR-ABL

1 2 3

tSN

E2

Phenograph Phenograph Phenograph

tSNE1

40

Fig 3.9

BCR-ABL

120324 827 9407

Ph-like 100513 260153 285035 100702

tSNE2

tSNE1

100709 795 90124 110833

Phenograph Phenograph Phenograph Phenograph

41

Fig 3.10

IgM

100513

260153

285035

100702

Ph-like

100709

795

90124

110833

120324

9407

827

11

BCR-ABL

63.411.36.74.13.1

% of total

39.26.76.66.55.4

35.521.510.58.57.335.221.88.86.34.1

36.319.76.66.45.5

65.99.364.43

10.710.18.74.42.9

% of total23.621.113.812.86.7

41.316.26.44.73.851.17.57.54.31.126.424.19.37.26.1

0

527

105204

12211125

1152

290

25

190

21

05376

08

2865

03481

04

810

6

60

1985

480

196

Cluster ClusterCD45

CD34

CD19

CD79

aCD

127

CD14

CD33

CD38

HLA

-DR

TSLP

R

-3.71 3.71-1.86 1.860

Arcsinh Ratio of Medians

CD45

CD34

CD19

CD79

aCD

127

CD14

CD33

CD38

HLA

-DR

TSLP

R

IgM

88.6%

64.4%

83.3%

76.2%

74.5%

88.6%

36.8%

78%

72.4%

71.5%

73.1%

42

Fig 3.11

A

P>0.99

NS

P=0.01

P=0.06

Cluster Enrichment Cluster Abundance

q=0.01 q=0.01

q=0.04

P=0.02

0 2 4 6 8

BCR-ABL

Ph-like

# Cases with PG 5

Present

Absent

q=0.02

NS

PG 2 PG 50

10

20

30

40%

of T

otal

Cel

ls

B Class Distinguishing Cluster Comparison

*** ***

Marker

MM

I

MM

I

C

Marker

D Secondary Class Distinguishing Clusters

CD45CD34CD19

CD79a

IgMCD127

TSLPRCD14CD33CD38

HLA-DR

0

50

100

150

200

Cluster 5 (BCR-ABL,7/7) Cluster 8 (Ph-like, 4/4)

*

*

*

***

**

43

Fig 3.12

44

Figure 3.7: Hierarchical clustering of phenotypic markers by sample group. MMIs of the

indicated phenotypic markers gated on leukemic cells from each sample were hierarchically

clustered using the R heatmaps.2 package. Each row was coloured based on the Z score which

indicates of signed fractional number of standard deviations that separate each sample MMI from

the row mean MMI. Samples were coloured according to their leukemic subtypes. (Ph-like

JAK2=dark blue, Ph-like CRLF2=light blue, Ph-like Other=green, BCR-ABL=black).

Figure 3.8: Unbiased identification of non-leukemic clusters. Live single cells from all samples

listed in Table 3.4.1 were clustered using the PG algorithm (k=30) based on expression of 12

phenotypic markers: CD45, CD34, CD19, CD3, CD79a, IgM, HLA-DR, CD14, CD33, CD38,

TSLPR, CD127. (A) Representative tSNE plots of 1 control PBMC sample coloured by CD3,

CD19 or CD33 intensity. Black arrows identify T, B and myeloid cell clusters. The t-SNE was

performed using 3,000 iterations, the 12 phenotypic markers and the PG cluster IDs. (B) Major

PG clusters in non-leukemic PBMC. Bar graphs show abundance (%) of the 32 PG clusters

identified in the 3 non-leukemic PBMC samples as determined by automatic cluster gating in

Cytobank. (C) Heatmap analysis of myeloid, B and T cell marker intensity in the 8 most abundant

PBMC PG clusters (rows). Heat-map shows expression levels of phenotypic markers (columns) in

the 8 most abundant clusters of a control PBMC samples. The heatmap is colored by the arcsinh

ratio of MMIs normalized to the column’s minimum.

Figure 3.9: PG tSNE visualization of PG clusters in non-leukemic vs leukemic PBMC

samples. tSNE plots of control PBMC from 3 experiments compared to 3 examples of BCR-ABL

samples and Ph-like CRLF2/JAK2 PBMC samples. Graphs are colored by PG cluster number. Ph-

like JAK2 and Ph-like CRLF2 sample IDs are coloured as previously described in Fig 3.6.

Figure 3.10: PG tSNE visualization of leukemic clusters in BCR-ABL vs Ph-like samples.

Clustering and tSNE analysis were performed as described in Figure 3.8. tSNE maps shown were

gated on the leukemic cell population (CD45lo CD34+) and coloured based by PG cluster number.

Ph-like sample IDs were coloured based on their subgroup as previously described.

45

Figure 3.11: Phenotypic marker intensity in the 5 most abundant PG leukemic clusters in

each sample. Heat maps compare the expression of phenotypic markers (CD45, CD34, CD19,

CD79a, IgM, CD127, TSLPR, CD14, CD33, CD38 and HLA-DR) in the top 5 most abundant

clusters in each sample. The % of the total live population in each cluster is listed to the right of

each heat map. Non-leukemic clusters identified in Fig 3.8 were excluded. Phenotypic markers are

separated by row and normalized by the arcsinh ratio to the column’s minimum within each

sample. All heat maps are scaled globally. Samples are separated by subgroups, BCR-ABL on the

left and Ph-like on the right. Ph-like samples are coloured based on subgroup as previously

described in Fig 3.4.1.

Figure 3.12: PG Cluster enrichment and abundance analysis revealed significantly different

BCR-ABL and Ph-like specific clusters. (A) Cluster enrichment analysis highlighted significant

differences between the prevalence of clusters 2, 5 and 8 in BCR-ABL and Ph-like samples. Bar

graphs show absence (grey) or presence (black) of each cluster in the top 5 most abundant clusters

in the two subgroups. P values from Fisher’s exact test are shown for comparison. (B) Abundance

of selected clusters between BCR-ABL and Ph-like samples. Bar graphs show the % of cells (out

of total live singlets) in each cluster per subgroup (BCR-ABL=white, Ph-like=grey). The line in the

middle of the boxplot represents the mean. In both A and B the FDR-adjusted Q-values from

multiple T-test are displayed. (C) Phenotypic differences in class distinguishing clusters. Graph

compares the mean MMI of phenotypic markers from the 7 BCR-ABL samples in cluster 5 (BC-

ABL class distinguishing) to the mean MMI from the 4 Ph-like samples in cluster 8 (Ph-like

specific). The top and bottom of each box highlights the 25th and 75th MMI percentile, the line

within each box represents the mean MMI and the whiskers indicate the minimum and maximum

MMI present. (D) Secondary class distinguishing clusters differed significantly in CD14

expression. Graphs compare mean MMI of phenotypic markers from 5/7 BCR-ABL samples in

cluster 2 (excluded samples 9407 and 827 because cluster was not present in their top 5 most

abundant) to the mean MMI of phenotypic markers from 3/4 Ph-like samples in cluster 4 (excluded

sample 285035) or cluster 6 (excluded sample 100513). In both Fig C and D and C *s are

indicative of FDR-adjusted Q-values from multiple t-tests. *** Q<0.001, ** Q=0.002, * Q=0.02.

46

3.5 Conclusions and Summary

In conclusion, I successfully built and optimized a 36-marker CyTOF panel and assay

which I used to identify Ph-like associated signaling and phenotypic marker expression. After

titrating each antibody, my final validation experiment, a BCR-ABL pilot, revealed surprising

heterogeneity in phenotypic marker expression and basal signaling activity in the BCR-ABL

subgroup. Following validation and optimization I profiled 7 BCR-ABL, 2 Ph-CRLF2, 1 Ph-like

JAK2 and 1 Ph-like Other, a 3 non-leukemic PBMC samples. Comparison of signaling activity

and phenotypic marker expression between the Ph-like and BCR-ABL subgroups revealed

significant differences. First, analysis of the signaling patterns suggested that this CyTOF

phospho-assay can identify leukemic blasts with activating ABL or JAK2 mutations based on

higher expression of pSTAT5 and its reduction by ABL- versus JAK2-selective TKis. Second,

BCR-ABL and Ph-like samples clustered distinctly from one another and mapped to opposite sides

of tSNE graphs suggesting phenotypic differences between the two subgroups. Cluster enrichment

and cluster abundance analysis identified BCR-ABL and Ph-like defining clusters which

significantly differed in expression of CD34, CD19, CD14, CD127 and HLA-DR. This analysis

demonstrates how CyTOF and unsupervised clustering algorithms can be used to identify

significant phenotypic differences between Ph-like and BCR-ABL B-ALL.

47

Chapter 4 Discussion

48

4.1 Overview

My thesis work focused on determining if CyTOF could be used to identify Ph-like specific

signaling patterns and/or phenotypic marker expression. To achieve this goal, I developed and

optimized a 36 marker CyTOF panel which included Abs specific for 12 phenotypic markers and

24 phosphorylated or intracellular markers. I chose the markers in the panel in order to identify

leukemic B cells, major cell lineages and downstream targets of key signaling pathways in B-ALL.

After building the panel, I titrated each antibody to their optimal dilution using cell lines or primary

samples. Before profiling Ph-like samples I wanted to ensure the assay and panel worked in

leukemic samples. Thus, I completed a pilot experiment with 3 BCR-ABL samples, a non-leukemic

PBMC control and the K-562 cell line +/- DAS and PV. Importantly, this pilot confirmed that my

panel could identify leukemic cells (CD45loCD34+) in viably frozen diagnostic PBMC samples

from leukemia patients. Interestingly, it also highlighted a surprising phenotypic heterogeneity in

the BCR-ABL samples. For example, intensity of CD45, CD34, CD19 and HLA-DR expression on

the leukemic blasts differed between the three samples. This pilot also showed variability in the

basal signaling activity in the 3 samples. For example, I observed different intensities of pSYK

and pSRC in the VEH-treated samples. However, in the three samples, DAS treatment

significantly decreased pSTAT5, pSRC and pSYK expression. I next used this validation panel and assay to profile signaling and phenotypic markers in

4 additional BCR-ABL samples (7 total), 2 Ph-like CRLF2, 1 Ph-like JAK2 and 1 Ph-like Other,

and 2 non-leukemic PBMC samples (3 total). As expected, the CD34+ leukemic cells in the BCR-

ABL samples exhibited high pSTAT5, as well high pSYK and pMTOR. pSTAT5 was more highly

expressed by CD34+ cells in the Ph-like JAK2 sample compared to the Ph-like CRLF2 samples.

Next, I assessed whether basal phosphorylation of STAT5 required active signaling via ABL or

JAK kinases. As expected, DAS treatment decreased pSTAT5 more robustly in the BCR-ABL

samples than in the Ph-like samples. While RUX treatment decreased pSTAT5 significantly more

robustly in Ph-like CRFL2/JAK2 samples than in BCR-ABL. These results confirm that basal

phosphorylation in more ABL or JAK2 dependent in BCR-ABL and Ph-like CRLF2/JAK2 samples

respectively.

Second, I used the unsupervised clustering algorithm PG to determine if Ph-like and BCR-

ABL samples expressed a different phenotypic profile. Analysis of this clustering revealed Ph-like

and BCR-ABL class and secondary class defining clusters which interestingly were often not the

49

most abundant cluster in each sample. The BCR-ABL samples expressed higher levels of CD14,

CD34, and CD19 in their class defining clusters compared to the Ph-like samples in their defining

cluster. In contrast, the Ph-like samples expressed higher levels of CD127 and HLA-DR in their

defining cluster. Overall, my thesis work demonstrates how CyTOF, and unsupervised clustering

algorithms, including PG, can be used to identify Ph-like specific patterns.

4.2 BCR-ABL Phenotypic and Signaling Heterogeneity My pilot BCR-ABL experiment revealed surprising heterogeneity in surface marker

expression of the leukemic blasts in BCR-ABL samples. For example, in sample 100709 I observed

a gradient of CD34 expression on the leukemic cell population. In contrast, the leukemic blasts in

sample 795 were uniformly CD34+. I also observed variation in expression of major lineage

markers including CD19. For example, the leukemic blasts in sample 90124 expressed overall low

levels of CD19 compared to samples 795 and 100709 whose blasts varied in CD19 intensity but

were both uniformly positive. Surface marker variability might be reflective of potential genetic

heterogeneity in the samples as the BCR-ABL subgroup is known to be quite genetically

heterogeneous. Multiple studies have identified non-prognostic secondary cytogenetically

abnormalities in BCR-ABL patients including hyperdiploidy (>50 chromosomes), loss of

chromosome 7 or 9, trisomy of chromosome 4 and supernumerary Ph71-72. Also, interstitial

chromosomal deletions are common in BCR-ABL samples and are undetectable by cytogenetic

analysis71-72. These additional alterations have been associated with variation in surface marker

expression. Primo et al. described a correlation between supernumerary Ph or trisomy 8 and higher

CD19, CD34, CD45 and HLA-DR expression on BCR-ABL leukemic blasts73. Conversely,

monosomy 7 correlated with lower CD19, CD34 and intracellular CD79a expression73. However,

as these additional mutations are either cytogenetically silent or non-prognostic they are often not

detected or reported in adult B-ALL. Despite all belonging to the same genetic subtype, the BCR-

ABL samples showed variation in expression of phenotypic markers which could be potentially

explained by undetectable additional genetic alterations. In additional to surface marker heterogeneity I also observed variation in the basal

signaling activity in the 3 BCR-ABL samples. First, as expected all three samples expressed high

levels of pSTAT5 comparable to the K-562 cell line33. In contrast, I observed variability in the

basal levels of pSYK and pSRC between the three samples. For example, my CyTOF phospho-

50

assay detected low levels of pSRC in sample 90124 but high expression in sample 795. One

plausible explanation for this variation is the genetic heterogeneity associated with BCR-ABL B-

ALL. Various additional cytogenetic abnormalities or mutations could be causing this diversity in

basal signaling. A second interesting explanation is epigenetic modification which are also

common in B-ALL. One example of an epigenetic modification common in ALL is DNA

methylation. Interestingly, hypermethylation of both SYK and SRC family genes has been found

in patients with B-ALL74-75. This hypermethylation results in silencing of genes and thus a decrease

in the protein levels. In conclusion, the signaling and phenotypic heterogeneity I observed in BCR-

ABL B-ALL could be associated with additional genetic alterations or epigenetic modifications.

4.3 RUX Sensitive pSTAT5 as a Ph-like CRLF2/JAK2 B-ALL Marker

My phospho-CyTOF assay identified higher levels of basal pSTAT5 in a Ph-like JAK2

sample compared to Ph-like CRLF2 samples. Previous studies have also identified elevated

pSTAT5 in Ph-like samples with PAX5-JAK2 fusions76. However, my study is the first to my

knowledge to directly compare basal phosphorylation in Ph-like CRLF2 and Ph-like JAK2

samples. My results suggest that a gene fusion involving JAK2 results in more potent basal

JAK/STAT signaling opposed to overexpression of the TSLPR heterodimer. My study is the first

to show that STAT5 is phosphorylated to a greater degree in a Ph-like JAK2 sample compared to

Ph-like CRLF2 samples.

Despite lower basal pSTAT5 in the two Ph-like CRLF2 samples, RUX-treatment still

significantly decreased pSTAT5 expression more potently than DAS-treatment in the Ph-like

CRLF2/JAK2 subgroup. This suggests that RUX-sensitive basal pSTAT5 could be used as a

biomarker for Ph-like CRLF2/JAK2 samples. This conclusion is in concordance with previous

work that has described inhibition of basal pSTAT5 in samples with the PAX5-JAK2 fusion. For

example, Roberts at al demonstrated, in a primary PAX5-JAK2 sample, that pSTAT5 decreased

more potently following RUX pre-treatment compared to DAS44. In contrast, previous studies with

Ph-like CRLF2 samples have focused largely on increased pSTAT5 following stimulation with the

TSLP cytokine77. For example, Brown et al. established that TSLP stimulation of cell lines

engineered with the TSLPR heterodimer results in phosphorylation of STAT578. However, my

study agrees with the only other study to my knowledge to describe RUX-sensitive pSTAT5 as a

marker for Ph-like CRLF2 samples. In a study by Tasian et al., the group treated 22 CRLF2+ adult

51

samples with RUX and quantified levels of phospho-proteins, including pSTAT5, by flow

cytometry79. This group also reported inhibition of pSTAT5 following RUX treatment which was

sustained following TSLP stimulation77. In conclusion, my work is in accordance with other

studies describing RUX-sensitive pSTAT5 as a marker for Ph-like CRLF2/JAK2 samples.

4.4 BCR-ABL Specific Clusters Expressed Significantly Higher CD14, CD34 and CD19

Analysis of the PG clustering revealed significant differences between BCR-ABL and Ph-

like samples in their respective class defining clusters, including higher CD14 in the BCR-ABL

specific clusters. Aberrant expression of myeloid markers in B-ALL has been observed in several

studies, with reports of up to 36% of B-ALL cases express myeloid markers80-81. Interestingly the

prognostic value of myeloid marker expression remains controversial. Some reports cite a lack of

impact80. In contrast, other groups have reported correlations between myeloid expression and

chemotherapy resistance83. However, there are likely two explanations as to why myeloid markers

are expressed on B-ALL blasts. First, the leukemic blasts stem from a progenitor cell that has not

yet differentiated into lymphoid or myeloid cells. Second, leukemic blasts are lymphoid cells or

progenitor cells that express both myeloid or lymphoid features, a phenomenon known as lineage

promiscuity or lineage infidelity84-85. The mean MMI of CD19 was also significantly higher in the

BCR-ABL class defining cluster, suggesting that this is a case of lineage infidelity. Interestingly,

the BCR-ABL subtype is associated with a high frequency of PAX5 and IKZF1 deletions84.

Deficiencies in the TFs encoded by these two genes results in an arrest in development at the pro-

B cell stage. Leukemic blasts arrested at this stage in development retain the capacity to lineage

reprogram into myeloid cells, including macrophages which are CD14+ 86-87. Furthermore, PAX5

deletions are less common in Ph-like (5%) compared to BCR-ABL (30%) B-ALL, which could

contribute to higher CD14 expression in BCR-ABL B-ALL88. In conclusion, aberrant myeloid

antigen expression is common in B-ALL and is a prime example of lineage infidelity.

In addition to CD14, the BCR-ABL samples expressed higher levels of CD34 in their class

defining cluster comparatively. In recent years, the prognostic value of CD34 in B-ALL has been

explored89-90. For example, Thomas et al. examined 75 B-ALL adult patients and concluded that

higher CD34 positivity was associated with features of poor prognosis in adult B-ALL91. In the

same study, higher CD34 expression was also significantly associated with higher expression of

CD19, a trend I also observed in the BCR-ABL specific cluster. In conclusion, the BCR-ABL

52

samples expressed significantly higher CD14, CD34 and CD19 in their class defining cluster

compared to the Ph-like samples in their defining cluster.

4.5 Ph-like Specific Clusters Expressed Significantly Higher CD127 and HLA-DR

The Ph-like specific samples expressed significantly higher CD127 in their class defining

cluster compared to the BCR-ABL samples in their respective cluster. CD127 (IL-7Ra) pairs with

the CRLF2 monomer to form the heterodimeric TSLPR which is present in approximately 50% of

Ph-like B-ALL92. As 2/4 of my Ph-like samples were CRLF2+ it is not overly surprisingly that

CD127 expression would be elevated in clusters abundant in Ph-like. It is unexpected that TSLPR

is not significantly different between the Ph-like and BCR-ABL significant clusters. However, this

variability is explained by the fact the not all my Ph-like samples were CRLF2+. It is interesting

that the 2 CRLF2- samples expressed higher levels of CD127 than the BCR-ABL samples. This

expression could be contributing to leukemogenesis, as it has been reported that CD127+ B-ALL

cells have higher expression of the anti-apoptotic protein Bcl-293-95. The 4 Ph-like samples, despite

their genetic subgrouping, expressed higher CD127 in their class defining cluster compared to the

BCR-ABL samples.

In addition to CD127, in their respective class defining clusters Ph-like samples expressed

higher HLA-DR comparatively. It has been reported that in up to 97% of B-ALL cases blasts

express HLA-DR on their surface96. Thus, HLA-DR expression on Ph-like blasts is unsurprising.

However, it is surprising that the BCR-ABL samples express such low levels of this marker.

Interestingly, a few cases of HLA-DR- B-ALL have been reported97. In conclusion, HLA-DR

expression on Ph-like leukemic blasts is in accordance with general phenotyping of B-ALL.

4.6 Class Defining Clusters Could be Subclones Present at Diagnosis

It is compelling that these class and secondary class distinguishing clusters are often not

the most abundant cluster in each sample. One potential explanation for this is that the class

defining clusters could be representative of sub-populations present at diagnosis. To date multiple

studies have reported the presence of subclones at diagnosis in B-ALL. For example, Obro et al.

reported that in a pediatric cohort of 41 BM diagnostic B-ALL samples, 15 expressed distinct

subclones98. Furthermore, using flow cytometry and a panel of phenotypic markers, Li et al.

identified 192 subpopulations in 23 B-ALL adult patients99. In this study, the group also profiled

53

paired samples from diagnosis and after the 1st course of induction therapy. Interestingly, following

induction therapy they detected new subclones not present at diagnosis in 22 of 23 patients99. Based

on this study I think in future experiments it would be interesting to include paired diagnostic and

relapse samples. It would be fascinating to track if new clusters (or subclones) appeared at relapse

or less predominant clusters at diagnosis increased in prevalence in the relapse sample. In previous

studies the capability of subclones to survive therapy, expand and cause future relapse has been

shown. For example, Notta et al. exhibited that often minor subclones outcompete the predominant

diagnostic clone in repopulation of mouse models xenografted with BCR-ABL diagnostic

samples100. Therefore, using CyTOF and clustering algorithms would be an interesting way to

further track the persistence of diagnostic subclones into relapse.

4.7 Conclusion and Future Direction In conclusion, I identified signaling pathway activation and a phenotypic profile associated

with Ph-like B-ALL. My CyTOF assay and panel, which I optimized in my first aim, revealed

surprisingly phenotypic and signaling heterogeneity in the BCR-ABL subgroup. This variability

could be explained by additional cytogenetic abnormalities or epigenetic modifications.

Furthermore, my CyTOF assay identified leukemic blasts with activating ABL or JAK2 mutations

based on their higher expression of basal pSTAT5 and its reduction by ABL- or JAK2-selective

tyrosine kinase inhibitors. My conclusion of RUX sensitive pSTAT5 as a marker for Ph-like

CRLF2/JAK2 samples is in concordance with previous studies published by Roberts et al. and

Taisan et al44,79.

PG clustering affirmed Ph-like and BCR-ABL specific phenotypic clusters which differed

significantly in expression of phenotypic markers such as CD14, CD34, CD19, CD127 and HLA-

DR. CD14 expression was significantly more intense in the BCR-ABL samples in their class

defining clusters compared to the Ph-like samples in theirs. This is an example of lineage infidelity

which is often observed in B-ALL. One explanation for this could be due to the high prevalence

of concurrent PAX5 mutations in BCR-ABL B-ALL. Higher CD127 expression in the Ph-like

specific cluster is expected as 2/4 of the Ph-like samples I profiled were CRLF2+. These significant

differences could also have prognostic value. For example, higher CD34 expression in B-ALL

blasts has been associated with poor prognosis in adults91. Lastly, these class defining PG clusters

were often not the most abundant cluster in each sample and could potentially be representative of

54

subclones present at diagnosis. In conclusion, my work demonstrates how CyTOF can be used to

identify Ph-like specific signaling and phenotypic patterns.

However, future experiments must be done to expand on these results. First, the sample

number must be expanded. Specifically, more Ph-like samples must be profiled. Importantly, Ph-

like ABL samples should be included in future studies. Unfortunately, these samples are quite rare

and difficult to find. Furthermore, samples that have complex or normal cytogenetics should also

be included for comparison. Second, I think the surface marker portion of my panel should be

expanded to include markers important in B cell development such as CD10 and CD20. I also

think more myeloid markers known to be aberrantly expressed in B-ALL, including CD13, should

be added. Expansion of the surface markers in the panel could help to further classify Ph-like

samples. Ultimately, if 4 to 5 markers could be identified as Ph-like specific, they could be used

clinically to rapidly identify Ph-like samples. Third, I think it would be interesting to compare

diagnostic and relapse samples to track if any clusters present at diagnosis either increased in

abundance or persisted to relapse. In conclusion, this work demonstrated how my CyTOF assay

along with unsupervised clustering algorithms can be used to classify specific phosphorylated

signatures and phenotypic expression in Ph-like B-ALL. This method could be further applied to

other HR genetic subtypes of B-ALL to improve our understanding of this heterogeneous

malignancy.

55

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